{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# project-based method with gc\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_unlearn_gc_20_39323.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "projector_gc_mean = np.mean(all_results, axis=0)\n",
    "projector_gc_stds = np.std(all_results, axis=0)\n",
    "\n",
    "# project-based method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_unlearn_20_39323.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "projector_mean = np.mean(all_results, axis=0)\n",
    "projector_stds = np.std(all_results, axis=0)\n",
    "\n",
    "\n",
    "# GraphSaint method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_retrain_graphsaint_20_39323.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "graphsaint_mean = np.mean(all_results, axis=0)\n",
    "graphsaint_stds = np.std(all_results, axis=0)\n",
    "\n",
    "\n",
    "# GAT method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_retrain_gat_20_39323.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "gat_mean = np.mean(all_results, axis=0)\n",
    "gat_stds = np.std(all_results, axis=0)\n",
    "\n",
    "# GraphSAGE method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_retrain_graphsage_20_39323.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "graphsage_mean = np.mean(all_results, axis=0)\n",
    "graphsage_stds = np.std(all_results, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"serif\" \n",
    "plt.rcParams[\"figure.figsize\"] = (6,2.5)\n",
    "\n",
    "SMALL_SIZE = 11\n",
    "MEDIUM_SIZE = 11\n",
    "BIGGER_SIZE = 12\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DATASET = 'OGB-Products'\n",
    "\n",
    "fig, axs = plt.subplots()\n",
    "######################################\n",
    "\n",
    "y_mean = projector_mean[:, -1]\n",
    "y_std  = projector_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=3, linestyle='-', label='Projector')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "######################################\n",
    "\n",
    "y_mean = projector_gc_mean[:, -1] \n",
    "y_std  = projector_gc_stds[:, -1] \n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=3, linestyle='-', label='Projector (+Adap diff)')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "######################################\n",
    "\n",
    "y_mean = graphsaint_mean[:, -1]\n",
    "y_std  = graphsaint_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=2, linestyle='--', label='GraphSaint')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "######################################\n",
    "\n",
    "y_mean = gat_mean[:, -1]\n",
    "y_std  = gat_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=2, linestyle='--', label='GAT')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "######################################\n",
    "y_mean = graphsage_mean[:, -1]\n",
    "y_std  = graphsage_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=2, linestyle='--', label='GraphSAGE')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "\n",
    "######################################\n",
    "plt.title('%s: Comparison on Testing accuracy'%DATASET, fontsize=15)\n",
    "axs.set_ylabel('F1-score', fontsize=15)\n",
    "axs.set_xlabel('Unlearn percentage (%)', fontsize=15)\n",
    "axs.xaxis.get_major_locator().set_params(integer=True)\n",
    "axs.grid(True)\n",
    "fig.tight_layout()\n",
    "axs.legend(fontsize=13)\n",
    "\n",
    "legend = axs.legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Macro-AUC', fontsize=13)\n",
    "plt.setp(legend.get_title(),fontsize=14)\n",
    "\n",
    "plt.savefig('%s_test_perform.pdf'%DATASET, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
